Manipulation Skill

Robotic manipulation skill research focuses on enabling robots to perform dexterous tasks, such as grasping, cutting, and assembling objects, with high accuracy and generalizability across diverse environments and objects. Current efforts concentrate on developing robust and efficient learning methods, often employing reinforcement learning, imitation learning, and diffusion models, sometimes combined with task and motion planning or geometric approaches to handle non-Euclidean data. These advancements are crucial for deploying robots in real-world settings, improving efficiency in manufacturing, logistics, and service industries, and furthering our understanding of embodied AI.